Toward a causal link between attachment styles and mental health during the COVID-19 pandemic.

BACKGROUND: Recent research has shown that insecure attachment, especially attachment anxiety, is associated with poor mental health outcomes, especially during the COVID-19 pandemic. Other research suggests that insecure attachment may be linked to nonadherence to social distancing behaviours during the pandemic. AIMS: The present study aims to examine the causal links between attachment styles (secure, anxious, avoidant), mental health outcomes (depression, anxiety, loneliness) and adherence to social distancing behaviours during the first several months of the UK lockdown (between April and August 2020). MATERIALS & METHODS: We used a nationally representative UK sample (cross-sectional n = 1325; longitudinal n = 950). The data were analysed using state-of-the-art causal discovery and targeted learning algorithms to identify causal processes. RESULTS: The results showed that insecure attachment styles were causally linked to poorer mental health outcomes, mediated by loneliness. Only attachment avoidance was causally linked to nonadherence to social distancing guidelines. DISCUSSION: Future interventions to improve mental health outcomes should focus on mitigating feelings of loneliness. Limitations include no access to pre-pandemic data and the use of categorical attachment measure. CONCLUSION: Insecure attachment is a risk factor for poorer mental health outcomes.

Uncovering the Most Important Factors for Predicting Sexual Desire Using Explainable Machine Learning.

BACKGROUND: Low sexual desire is the most common sexual problem reported with 34% of women and 15% of men reporting lack of desire for at least 3 months in a 12-month period. Sexual desire has previously been associated with both relationship and individual well-being highlighting the importance of understanding factors that contribute to sexual desire as improving sexual desire difficulties can help improve an individual's overall quality of life. AIM: The purpose of the present study was to identify the most salient individual (eg, attachment style, attitudes toward sexuality, gender) and relational (eg, relationship satisfaction, sexual satisfaction, romantic love) predictors of dyadic and solitary sexual desire from a large number of predictor variables. METHODS: Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. We used a machine learning algorithm, random forest (a type of highly non-linear decision tree), to circumvent these issues to predict dyadic and solitary sexual desire from a large number of predictors across 2 online samples (N = 1,846; includes 754 individuals forming 377 couples). We also used a Shapley value technique to estimate the size and direction of the effect of each predictor variable on the model outcome. OUTCOMES: The outcomes included total, dyadic, and solitary sexual desire measured using the Sexual Desire Inventory. RESULTS: The models predicted around 40% of variance in dyadic and solitary desire with women's desire being more predictable than men's overall. Several variables consistently predicted dyadic sexual desire such as sexual satisfaction and romantic love, and solitary desire such as masturbation and attitudes toward sexuality. These predictors were similar for both men and women and gender was not an important predictor of sexual desire. CLINICAL TRANSLATION: The results highlight the importance of addressing overall relationship satisfaction when sexual desire difficulties are presented in couples therapy. It is also important to understand clients' attitudes toward sexuality. STRENGTHS & LIMITATIONS: The study improves on existing methodologies in the field and compares a large number of predictors of sexual desire. However, the data were cross-sectional and there may have been variables that are important for desire but were not present in the datasets. CONCLUSION: Higher sexual satisfaction and feelings of romantic love toward one's partner are important predictors of dyadic sexual desire whereas regular masturbation and more permissive attitudes toward sexuality predicted solitary sexual desire. Vowels LM, Vowels MJ, Mark KP. Uncovering the Most Important Factors for Predicting Sexual Desire Using Explainable Machine Learning. J Sex Med 2021;18:1198-1216.

What can be learned from couple research: Examining emotional co-regulation processes in face-to-face interactions.

A crucial component of successful counseling and psychotherapy is the dyadic emotion co-regulation process between patient and therapist that unfolds moment to moment during therapy sessions. The major reason for the disappointing progress in understanding this process is the lack of appropriate methods to assess subjectively experienced emotions continuously during therapy sessions without disturbing the natural flow of the interaction. The resulting inability has forced the field to focus on patients' overall emotion ratings at the end of each session with limited predictive value of the dyadic interplay between patient and therapist's emotional states within each session. The current tutorial demonstrates how couple research-confronted with a comparable problem-has overcome this issue by (i) developing a video-based retrospective self-report assessment method for individuals' continuous state emotions without undermining the dyadic interaction and (ii) using a validated statistical tool to analyze the dynamical process during a dyadic interaction. We show how to assess emotion data continuously, and how to unravel self-regulation and co-regulation processes using a Latent Differential Equation Modeling approach. Finally, we discuss how this approach can be applied in counseling psychology and psychotherapy to test basic theoretical assumptions about the co-creation of emotions despite the conceptual differences between couple dyads and therapist-patient dyads. The present method aims to inspire future research activities examining systematic real-time processes between patients and therapists. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Trying to outrun causality with machine learning: Limitations of model explainability techniques for exploratory research.

Machine learning explainability techniques have been proposed as a means for psychologists to "explain" or interrogate a model in order to gain an understanding of a phenomenon of interest. Researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables that are associated with/predictive of an outcome of interest. However, and as we demonstrate, machine learning algorithms are highly sensitive to the underlying causal structure in the data. The consequences of this are that predictors which are deemed by the explainability technique to be unrelated/unimportant/unpredictive, may actually be highly associated with the outcome. Rather than this being a limitation of explainability techniques per se, we show that it is rather a consequence of the mathematical implications of regression, and the interaction of these implications with the associated conditional independencies of the underlying causal structure. We provide some alternative recommendations for psychologists wanting to explore the data for important variables. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Longitudinal evidence over 2 years of the pandemic shows that poor mental health in people living with obesity may be underestimated.

It is well-documented that people living with obesity are at greater risk of poorer mental health outcomes. The aim of our study was twofold: First, to examine the longitudinal trajectories of depression and anxiety in people living with obesity over two years across eight waves of a UK national COVID-19 survey (March 2020-March 2022) using smoothing-splines mixed-effects models. Second, to investigate participation effects via a missingness analysis to check whether survey attrition over time was related to participant characteristics. Trajectory models showed that those living with overweight and obesity consistently reported significantly higher rates of anxiety and depression compared to those in normal weight categories over two years. Our missingness analysis revealed that depression and anxiety predicted the likelihood of responding to the survey over time, whereby those reporting higher rates of depression and anxiety were less likely to respond to the survey. Our findings add to the literature surrounding the (long-term) link between living with obesity and poor mental health. Notably, our results suggest that people who have poorer mental health were less likely to participate in the survey. Thus, we conclude that it is likely that longitudinal population survey studies potentially underreport mental health problems over time and therefore the realistic impact of obesity on mental health outcomes may be underestimated.

Misspecification and unreliable interpretations in psychology and social science.

The replicability crisis has drawn attention to numerous weaknesses in psychology and social science research practice. In this work we focus on three issues that cannot be addressed with replication alone, and which deserve more attention: Functional misspecification, structural misspecification, and unreliable interpretation of results. We demonstrate a number of possible consequences via simulation, and provide recommendations for researchers to improve their research practice. Psychologists and social scientists should engage with these areas of analytical and statistical improvement, as they have the potential to seriously hinder scientific progress. Every research question and hypothesis may present its own unique challenges, and it is only through an awareness and understanding of varied statistical methods for predictive and causal modeling, that researchers will have the tools with which to appropriately address them. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Spectral and cross-spectral analysis-A tutorial for psychologists and social scientists.

Social scientists have become increasingly interested in using intensive longitudinal methods to study social phenomena that change over time. Many of these phenomena are expected to exhibit cycling fluctuations (e.g., sleep, mood, sexual desire). However, researchers typically employ analytical methods which are unable to model such patterns. We present spectral and cross-spectral analysis as means to address this limitation. Spectral analysis provides a means to interrogate time series from a different, frequency domain perspective, and to understand how the time series may be decomposed into their constituent periodic components. Cross-spectral extends this to dyadic data and allows for synchrony and time offsets to be identified. The techniques are commonly used in the physical and engineering sciences, and we discuss how to apply these popular analytical techniques to the social sciences while also demonstrating how to undertake estimations of significance and effect size. In this tutorial we begin by introducing spectral and cross-spectral analysis, before demonstrating its application to simulated univariate and bivariate individual- and group-level data. We employ cross-power spectral density techniques to understand synchrony between the individual time series in a dyadic time series, and circular statistics and polar plots to understand phase offsets between constituent periodic components. Finally, we present a means to undertake nonparameteric bootstrapping in order to estimate the significance, and derive a proxy for effect size. A Jupyter Notebook (Python 3.6) is provided as supplementary material to aid researchers who intend to apply these techniques. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Is Infidelity Predictable? Using Explainable Machine Learning to Identify the Most Important Predictors of Infidelity.

Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners' well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity across two studies (one individual and one dyadic, N = 1,295). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall and interpersonal factors such as relationship satisfaction, love, desire, and relationship length were the most predictive of online and in person infidelity. The results suggest that addressing relationship difficulties early in the relationship may help prevent infidelity.

Using spectral and cross-spectral analysis to identify patterns and synchrony in couples' sexual desire.

Sexual desire discrepancy is one of the most frequently reported sexual concerns for individuals and couples and has been shown to be negatively associated with sexual and relationship satisfaction. Sexual desire has increasingly been examined as a state-like construct that ebbs and flows, but little is known about whether there are patterns in the fluctuation of sexual desire. Utilizing spectral and cross-spectral analysis, we transformed 30 days of dyadic daily diary data for perceived levels of sexual desire for a non-clinical sample of 133 couples (266 individuals) into the frequency domain to identify shared periodic state fluctuations in sexual desire. Spectral analysis is a technique commonly used in physics and engineering that allows time series data to be analyzed for the presence of regular cycles of fluctuation. Cross-spectral analysis allows for dyadic data to be analyzed for shared rates of fluctuation between partners as well as the degree of (a)synchrony (or phase shift) between these fluctuations. Men and women were found to exhibit fluctuations in sexual desire at various frequencies including rates of once and twice per month, and to have sexual desire that was unlikely to fluctuate over periods of three days or less and therefore exhibited persistence. Similar patterns of fluctuation were exhibited within couples and these patterns were found to be largely synchronous. While instances of desire discrepancy may arise due to differences in rates of sexual desire fluctuation and random fluctuations, such instances may be normal for romantic relationships. The results have important implications for researchers, clinicians, and educators in that they corroborate the supposition that sexual desire ebbs and flows and suggest that it does so with predictable regularity.